TESI DOCTORAL - La Salle

B.1. Clustering indeterminacies in unimodal data sets
Data set Data representation
name Baseline PCA ICA NMF RP
Zoo 28 392 392 392 392
Iris 28 56 56 56 56
Wine 28 308 308 308 308
Glass 28 196 196 196 196
Ionosphere 28 896 896 - 896
WDBC 28 784 784 784 784
Balance 28 56 - 56 56
Mfeat
28 on each of its 6 representations
(FAC, FOU, KAR, MOR, PIX and ZER)
miniNG 28 504 504 504 504
Segmentation 28 476 476 - 476
BBC 28 392 392 392 392
PenDigits 28 392 392 392 392
Table B.1: Number of individual clusterings per data representation on each unimodal data
set.
the ICA algorithm employed for deriving the homonymous object representation presented
convergence problems when executed on the Balance data collection, so no ICA representation
was created on this data set.
In the next paragraphs, we describe the clustering results obtained on each data set,
emphasizing which clustering configurations lead to the best clustering results in each case.
B.1.1 Zoo data set
Figure B.1 presents the histograms of the φ (NMI) values (ranging in the [0,1] interval) obtained
by all the clustering algorithms on each data representation for the Zoo data collection.
Recall that φ (NMI) = 1 corresponds to a perfect match between the ground truth and
a clustering solution. The analysis of these histograms help us to interpret the influence of
the clustering indeterminacies on the quality of the clustering results.
clustering count
30
20
10
Zoo Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
clustering count
30
20
10
Zoo PCA
0
0 0.5 1
φ (NMI)
(b) PCA
clustering count
30
20
10
Zoo ICA
0
0 0.5 1
φ (NMI)
(c) ICA
clustering count
30
20
10
Zoo NMF
0
0 0.5 1
φ (NMI)
(d) NMF
clustering count
30
20
10
Zoo RP
0
0 0.5 1
φ (NMI)
(e) RP
Figure B.1: Histograms of the φ (NMI) values obtained on each data representation in the
Zoodataset.
Firstly, by inspecting the histogram corresponding to the clustering results obtained by
applying the 28 algorithms on the baseline object representation (figure B.1(a)), we can
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